--- title: dashboards keywords: fastai sidebar: home_sidebar summary: "Supplies dashboards to investigate datasets and training results. Dashboards are defined as classes, to show the dashboard use the .show() function on an dashboard instance." description: "Supplies dashboards to investigate datasets and training results. Dashboards are defined as classes, to show the dashboard use the .show() function on an dashboard instance." nb_path: "nbs/dashboards.ipynb" ---
import icedata
test_data_dir = icedata.fridge.load_data()
test_class_map = icedata.fridge.class_map()
test_parser = icedata.fridge.parser(test_data_dir)
test_train_records, test_valid_records = test_parser.parse()
test_valid_record_dataset = BboxRecordDataset(test_valid_records, test_class_map)
test_train_record_dataset = BboxRecordDataset(test_train_records, test_class_map)
test_very_large_record_dataset = BboxRecordDataset(test_valid_records*10, test_class_map)
test_record_dataset_no_class_map = BboxRecordDataset(test_train_records)
import copy
from pathlib import Path
long_record_list = [copy.deepcopy(test_valid_records[0]) for i in range(40_000)]
for index, record in enumerate(long_record_list):
record.imageid = index
record.filepath = Path(str("imgs/"+str(index)+".jpg"))
record.width = np.random.randint(1, 10_000)
long_record_dataset = BboxRecordDataset(long_record_list, test_class_map)
test_object_detection_overview = ObjectDetectionDatasetOverview(test_valid_record_dataset, width=1500, height=900)
test_object_detection_overview.show()
test_object_detection_comparison = ObjectDetectionDatasetComparison([test_valid_record_dataset, test_train_record_dataset], width=1700, height=700)
test_object_detection_comparison.show()
test_dataset_generator = ObjectDetectionDatasetGeneratorScatter(test_valid_record_dataset, height=700, width=1000)
test_dataset_generator.show()
test_dataset_generator = ObjectDetectionDatasetGeneratorRange(test_valid_record_dataset, height=700, width=1000)
test_dataset_generator.show()
odrd = ObjectDetectionResultsDataset.load("test_data/fridge_train.dat")
odrdash = ObjectDetectionResultOverview(odrd, width=1500)
odrdash.show()